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ACE-ProtoNet: Adaptive covariance eigen-gate and uncertainty-aware prototype learning for coronary artery
Caixia Dong1, Duwei Dai1, Pengyu Ren2
1National-Local Joint Engineering Research Center of Biodiagnosis & Biotherapy, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China; School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China; Institute of Medical Artificial Intelligence, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China.
We developed ACE-ProtoNet for accurate coronary artery segmentation in Coronary CT Angiography (CCTA). This novel framework significantly improves segmentation accuracy and robustness for better cardiac imaging analysis.
Area of Science:
- Medical Imaging
- Computer Vision
- Artificial Intelligence
Background:
- Accurate segmentation of coronary arteries from Coronary CT Angiography (CCTA) is crucial for diagnosing cardiovascular diseases.
- Challenges include low contrast, anatomical variability, and complex vessel structures, hindering automated segmentation.
Purpose of the Study:
- To introduce ACE-ProtoNet, a novel framework for robust and accurate coronary artery segmentation.
- To address limitations of existing automated segmentation methods in CCTA.
Main Methods:
- A parallel dual-encoder backbone combining a Vision Foundation Model (VFM) and a CNN.
- An Adaptive Covariance Eigen-Gate (ACE-Gate) for feature integration.
- An Uncertainty-aware Prototype Learning Head (UPL-Head) for enhanced representation.
Main Results:
- ACE-ProtoNet achieved superior performance compared to twelve state-of-the-art methods across multiple metrics.
- Demonstrated strong cross-domain generalization, cross-modality, and cross-anatomy transferability.
- Significantly improved segmentation accuracy in challenging regions.
Conclusions:
- ACE-ProtoNet offers a robust and accurate solution for coronary artery segmentation in CCTA.
- The framework shows promise for advancing quantitative stenosis evaluation and surgical planning.
- The proposed methods enhance feature integration and representation learning for medical image analysis.
